Principal Data Scientist, GTM Data Science

About SimpliSafe We’re a high-tech home security company that’s passionate about protecting the life you’ve built and our mission of keeping Every Home Secure. And we’ve created a culture here that cares just as deeply about the career you’re building. Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect. We don’t just want you to work here. We want you to grow and thrive here. We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done.  Why are we hiring? Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure.  Role Overview As Principal Data Scientist, GTM Data Science, you will report to the Senior Director of Data Science and serve as a senior technical leader for growth, marketing, customer lifecycle, and personalization. You will shape the strategy and production systems that help SimpliSafe acquire, retain, and grow subscriber relationships while improving the customer experience. What We’re Looking For Deep expertise in marketing science, customer personalization, lifecycle modeling, and applied machine learning. Strong hands-on experience building and deploying machine learning solutions that have driven measurable business outcomes in production. Experience developing models and decision systems for customer acquisition, conversion, retention, segmentation, personalization, lifetime value, and next-best-action use cases. Strong understanding of experimentation, causal inference, incrementality measurement, and marketing effectiveness. Significant experience with MLOps, model deployment, model monitoring, and distributed compute environments. Proficiency with Python, SQL, Databricks, AWS SageMaker, Spark, MLflow, and modern cloud data platforms. Experience designing scalable data and feature pipelines for production ML systems. Ability to operate as a senior individual contributor: setting technical direction, influencing roadmaps, mentoring others, and driving execution across teams. Strong communication skills, with the ability to explain complex modeling concepts to technical and non-technical stakeholders. Typically 6+ years of experience in data science, machine learning, applied statistics, or a related field, or equivalent demonstrated expertise. Preferred Qualifications Hands-on experience with advanced ML techniques such as Gradient Boosted Trees, neural net...